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White Box Classification of Dissimilarity Data

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Hybrid Artificial Intelligent Systems (HAIS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7208))

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Abstract

While state-of-the-art classifiers such as support vector machines offer efficient classification for kernel data, they suffer from two drawbacks: the underlying classifier acts as a black box which can hardly be inspected by humans, and non-positive definite Gram matrices require additional preprocessing steps to arrive at a valid kernel. In this approach, we extend prototype-based classification towards general dissimilarity data resulting in a technology which (i) can deal with dissimilarity data characterized by an arbitrary symmetric dissimilarity matrix, (ii) offers intuitive classification in terms of prototypical class representatives, and (iii) leads to state-of-the-art classification results.

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Author information

Authors and Affiliations

  1. CITEC Centre of Excellence, Bielefeld University, 33615, Bielefeld, Germany

    Barbara Hammer, Bassam Mokbel, Frank-Michael Schleif & Xibin Zhu

Authors
  1. Barbara Hammer
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  2. Bassam Mokbel
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  3. Frank-Michael Schleif
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  4. Xibin Zhu
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Editor information

Editors and Affiliations

  1. Universidad de Salamanca, Plaza de la Merced S/N, 37008, Salamanca, Spain

    Emilio Corchado

  2. VÅ B-TU Ostrava 17, Listopadu 15, 70833, Ostrava, Czech Republic

    Václav Snášel

  3. Machine Intelligence Research Labs(MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071, Auburn, Washington, USA

    Ajith Abraham

  4. Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370, Wroclaw, Poland

    Michał Woźniak

  5. University of the Basque Country, Pº Manuel Lardizabal 1, 20018, San Sebastian, Spain

    Manuel Graña

  6. Yonsei University, 134 Shinchon-dong, Sudaemoon-ku, 120-749, Seoul, Korea

    Sung-Bae Cho

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© 2012 Springer-Verlag Berlin Heidelberg

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Hammer, B., Mokbel, B., Schleif, FM., Zhu, X. (2012). White Box Classification of Dissimilarity Data. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, SB. (eds) Hybrid Artificial Intelligent Systems. HAIS 2012. Lecture Notes in Computer Science(), vol 7208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28942-2_28

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  • DOI: https://doi.org/10.1007/978-3-642-28942-2_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-28941-5

  • Online ISBN: 978-3-642-28942-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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